Role purpose
The MLOps Engineer is a developer with solid experience in systems management, agile methodology, and operational-ready products (DevOps). This role contributes to the development of our Data Science and ML Ops Platform, applying efficient development practices with full-stack proficiency. Team collaboration and leadership potential are key success factors, alongside effective stakeholder engagement and interaction with agronomists and product owners to deliver business impact.
Knowledge, experience & capabilities
Experience
- 4+ years of professional software development experience
- 3+ years hands-on MLOps, DevOps, or platform engineering experience
- Demonstrated experience delivering production ML systems or data platforms
- Track record of working in cross-functional teams
Core Engineering Skills
- Strong full-stack development: ReactJS with Python, Java, or Node.js backends
- Proficient in SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, DynamoDB) databases
- Solid CI/CD automation experience: Jenkins, GitLab CI, GitHub Actions, automated testing
- RESTful API design and implementation following industry standards
- Microservices architecture and containerization with Docker/Kubernetes
MLOps & Cloud
- Hands-on experience with MLOps frameworks: MLflow, Kubeflow, SageMaker, or similar
- AWS DS & AI Ecosystems
- AWS cloud services: EC2, S3, Lambda, ECS/EKS, model deployment pipelines
- Infrastructure as Code basics: Terraform or CloudFormation
- Agile/Scrum methodology with sprint delivery experience
- Experience mentoring junior engineers or leading small technical initiatives
Critical success factors & key challenges
Technical Execution
- Strong algorithm design and problem-solving capabilities
- Build and deliver Infrastructure, environment and pipelines for DS, ML and AI Solutions
- Support prioritization of business initiatives across complex technical landscapes
Collaboration & Communication
- Explain technical concepts clearly to non-technical stakeholders including agronomists
- Work effectively across data science, engineering, and business teams
- Contribute to technical documentation and knowledge sharing
Growth & Leadership
- Demonstrate problem-solving and sound decision-making skills
- Show teamwork, emerging leadership abilities, and mentorship potential
- Adapt quickly in dynamic environments with evolving requirements
Innovations
Employee may, as part of his/her role and maybe through multifunctional teams, participate in the creation and design of innovative solutions. In this context, Employee may contribute to inventions, designs, other work product, including know-how, copyrights, software, innovations, solutions, and other intellectual assets.